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phoatis.py

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+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import datasets
+
+from seacrowd.utils import schemas
+from seacrowd.utils.configs import SEACrowdConfig
+from seacrowd.utils.constants import Tasks, Licenses
+
+_CITATION = """\
+@article{dao2021intent,
+      title={Intent Detection and Slot Filling for Vietnamese},
+      author={Mai Hoang Dao and Thinh Hung Truong and Dat Quoc Nguyen},
+      year={2021},
+      eprint={2104.02021},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+"""
+
+_DATASETNAME = "phoatis"
+
+_DESCRIPTION = """\
+This is first public intent detection and slot filling dataset for Vietnamese. The data contains 5871 English utterances from ATIS that are manually translated by professional translators into Vietnamese.
+"""
+
+_HOMEPAGE = "https://github.com/VinAIResearch/JointIDSF/"
+
+_LICENSE = Licenses.UNKNOWN.value
+
+_URLS = {
+    _DATASETNAME: {
+        "syllable": {
+            "syllable_train": [
+                "https://raw.githubusercontent.com/VinAIResearch/JointIDSF/main/PhoATIS/syllable-level/train/seq.in",
+                "https://raw.githubusercontent.com/VinAIResearch/JointIDSF/main/PhoATIS/syllable-level/train/seq.out",
+                "https://raw.githubusercontent.com/VinAIResearch/JointIDSF/main/PhoATIS/syllable-level/train/label",
+            ],
+            "syllable_dev": [
+                "https://raw.githubusercontent.com/VinAIResearch/JointIDSF/main/PhoATIS/syllable-level/dev/seq.in",
+                "https://raw.githubusercontent.com/VinAIResearch/JointIDSF/main/PhoATIS/syllable-level/dev/seq.out",
+                "https://raw.githubusercontent.com/VinAIResearch/JointIDSF/main/PhoATIS/syllable-level/dev/label",
+            ],
+            "syllable_test": [
+                "https://raw.githubusercontent.com/VinAIResearch/JointIDSF/main/PhoATIS/syllable-level/test/seq.in",
+                "https://raw.githubusercontent.com/VinAIResearch/JointIDSF/main/PhoATIS/syllable-level/test/seq.out",
+                "https://raw.githubusercontent.com/VinAIResearch/JointIDSF/main/PhoATIS/syllable-level/test/label",
+            ],
+        },
+        "word": {
+            "word_train": [
+                "https://raw.githubusercontent.com/VinAIResearch/JointIDSF/main/PhoATIS/word-level/train/seq.in",
+                "https://raw.githubusercontent.com/VinAIResearch/JointIDSF/main/PhoATIS/word-level/train/seq.out",
+                "https://raw.githubusercontent.com/VinAIResearch/JointIDSF/main/PhoATIS/word-level/train/label",
+            ],
+            "word_dev": [
+                "https://raw.githubusercontent.com/VinAIResearch/JointIDSF/main/PhoATIS/word-level/dev/seq.in",
+                "https://raw.githubusercontent.com/VinAIResearch/JointIDSF/main/PhoATIS/word-level/dev/seq.out",
+                "https://raw.githubusercontent.com/VinAIResearch/JointIDSF/main/PhoATIS/word-level/dev/label",
+            ],
+            "word_test": [
+                "https://raw.githubusercontent.com/VinAIResearch/JointIDSF/main/PhoATIS/word-level/test/seq.in",
+                "https://raw.githubusercontent.com/VinAIResearch/JointIDSF/main/PhoATIS/word-level/test/seq.out",
+                "https://raw.githubusercontent.com/VinAIResearch/JointIDSF/main/PhoATIS/word-level/test/label",
+            ],
+        },
+    }
+}
+
+_LOCAL = False
+_LANGUAGES = ["vie"]
+
+_SUPPORTED_TASKS = [Tasks.INTENT_CLASSIFICATION, Tasks.SLOT_FILLING]
+
+_SOURCE_VERSION = "1.0.0"
+
+_SEACROWD_VERSION = "2024.06.20"
+
+
+def config_constructor_intent_cls(schema: str, version: str, phoatis_subset: str = "syllable") -> SEACrowdConfig:
+    assert phoatis_subset == "syllable" or phoatis_subset == "word"
+
+    return SEACrowdConfig(
+        name="phoatis_intent_cls_{phoatis_subset}_{schema}".format(phoatis_subset=phoatis_subset.lower(), schema=schema),
+        version=version,
+        description="PhoATIS Intent Classification: {subset} {schema} schema".format(subset=phoatis_subset, schema=schema),
+        schema=schema,
+        subset_id=phoatis_subset,
+    )
+
+
+def config_constructor_slot_filling(schema: str, version: str, phoatis_subset: str = "syllable") -> SEACrowdConfig:
+    assert phoatis_subset == "syllable" or phoatis_subset == "word"
+
+    return SEACrowdConfig(
+        name="phoatis_slot_filling_{phoatis_subset}_{schema}".format(phoatis_subset=phoatis_subset.lower(), schema=schema),
+        version=version,
+        description="PhoATIS Slot Filling: {subset} {schema} schema".format(subset=phoatis_subset, schema=schema),
+        schema=schema,
+        subset_id=phoatis_subset,
+    )
+
+
+class PhoATIS(datasets.GeneratorBasedBuilder):
+    """This is first public intent detection and slot filling dataset for Vietnamese. The data contains 5871 English utterances from ATIS that are manually translated by professional translators into Vietnamese."""
+
+    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
+    SEACROWD_VERSION = datasets.Version(_SEACROWD_VERSION)
+
+    # BUILDER_CONFIGS = [config_constructor_intent_cls("source", _SOURCE_VERSION, subset) for subset in ["syllable", "word"]]
+    BUILDER_CONFIGS = []
+    BUILDER_CONFIGS.extend([config_constructor_intent_cls("seacrowd_text", _SEACROWD_VERSION, subset) for subset in ["syllable", "word"]])
+    # BUILDER_CONFIGS.extend([config_constructor_slot_filling("source", _SOURCE_VERSION, subset) for subset in ["syllable", "word"]])
+    BUILDER_CONFIGS.extend([config_constructor_slot_filling("seacrowd_seq_label", _SEACROWD_VERSION, subset) for subset in ["syllable", "word"]])
+
+    BUILDER_CONFIGS.extend(
+        [  # Default config
+            SEACrowdConfig(
+                name="phoatis_source",
+                version=SOURCE_VERSION,
+                description="PhoATIS source schema (Syllable version)",
+                schema="source",
+                subset_id="syllable",
+            ),
+            SEACrowdConfig(
+                name="phoatis_intent_cls_seacrowd_text",
+                version=SEACROWD_VERSION,
+                description="PhoATIS Intent Classification SEACrowd schema (Syllable version)",
+                schema="seacrowd_text",
+                subset_id="syllable",
+            ),
+            SEACrowdConfig(
+                name="phoatis_slot_filling_seacrowd_seq_label",
+                version=SEACROWD_VERSION,
+                description="PhoATIS Slot Filling SEACrowd schema (Syllable version)",
+                schema="seacrowd_seq_label",
+                subset_id="syllable",
+            ),
+        ]
+    )
+
+    DEFAULT_CONFIG_NAME = "phoatis_source"
+
+    def _info(self) -> datasets.DatasetInfo:
+
+        if self.config.schema == "source":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "intent_label": datasets.Value("string"),
+                    "slot_label": datasets.Sequence(datasets.Value("string")),
+                }
+            )
+
+        elif self.config.schema == "seacrowd_text":
+            with open("./seacrowd/sea_datasets/phoatis/intent_label.txt", "r+", encoding="utf8") as fw:
+                intent_label = fw.read()
+                intent_label = intent_label.split("\n")
+            features = schemas.text_features(intent_label)
+
+        elif self.config.schema == "seacrowd_seq_label":
+            with open("./seacrowd/sea_datasets/phoatis/slot_label.txt", "r+", encoding="utf8") as fw:
+                slot_label = fw.read()
+                slot_label = slot_label.split("\n")
+            features = schemas.seq_label_features(slot_label)
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        schema = self.config.subset_id
+        urls = _URLS[_DATASETNAME][schema]
+        data_dir = dl_manager.download_and_extract(urls)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_dir[f"{schema}_train"],
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "filepath": data_dir[f"{schema}_test"],
+                    "split": "test",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "filepath": data_dir[f"{schema}_dev"],
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath: Path, split: str) -> Tuple[int, Dict]:
+        with open(filepath[0], "r+", encoding="utf8") as fw:
+            data_input = fw.read()
+            data_input = data_input.split("\n")
+        with open(filepath[1], "r+", encoding="utf8") as fw:
+            data_slot = fw.read()
+            data_slot = data_slot.split("\n")
+        with open(filepath[2], "r+", encoding="utf8") as fw:
+            data_intent = fw.read()
+            data_intent = data_intent.split("\n")
+
+        if self.config.schema == "source":
+            for idx, text in enumerate(data_input):
+                example = {}
+                example["id"] = str(idx)
+                example["text"] = text
+                example["intent_label"] = data_intent[idx]
+                example["slot_label"] = data_slot[idx].split()
+                yield example["id"], example
+
+        elif self.config.schema == "seacrowd_text":
+            for idx, text in enumerate(data_input):
+                example = {}
+                example["id"] = str(idx)
+                example["text"] = text
+                example["label"] = data_intent[idx]
+                yield example["id"], example
+
+        elif self.config.schema == "seacrowd_seq_label":
+            for idx, text in enumerate(data_input):
+                example = {}
+                example["id"] = str(idx)
+                example["tokens"] = text.split()
+                example["labels"] = data_slot[idx].split()
+                yield example["id"], example